Techniques to determine the resistance of a pathogen or malignant cell to a therapy are becoming increasingly important. For example, despite the great advantages of existing treatments against viral infections such as HIV infection, cancer and bacterial infections, many patients experience treatment failure or reduced efficacy over time. In many instances this is due to the pathogen, malignant cell, bacteria, virus or other disease state mutating and/or developing a resistance to the treatment.
For example, all the drugs currently used in the HIV field were discovered and developed over a period of 15 years, starting with AZT. By the beginning of the year 2000, 15 different anti-HIV-1 agents had been approved by the FDA. Initially, and due to a lack of alternative drugs, these agents were administered alone, as monotherapy. Though a temporary antiviral effect was observed, all the compounds lost their effectiveness over time. In 1989, Larder et al. published a paper in Science, 246, 1155–8, incorporated by reference herein, that identified a number of mutations that caused HIV-1 resistance to AZT. Since then, research has demonstrated that one of the main reasons behind treatment failure for all the antiviral drugs is the development of resistance of the virus to the drug.
Drug resistance and drug resistant mutations develop because retroviruses such as HIV have no proofreading mechanism when synthesizing new nucleic acid strands. This allows for the continuous generation of a number of genetic variants in a replicating viral population. More importantly, the genetic changes may alter the configuration of the reverse transcriptase (RT) and protease (PR) molecules in such a way that they are no longer susceptible to inhibition by compounds developed to target them. If antiretroviral therapy is ongoing and if viral replication is not completely suppressed, the selection of genetic variants is inevitable and the viral population becomes resistant to the drug.
In the face of monotherapy failure and encouraged by a number of clinical trials, in the early-mid 1990's treatment strategy turned to combination therapy, i.e., administration of mixtures of antiviral drugs. At the time there were still only one class of drugs available—the nucleoside analogue reverse transcriptase inhibitors (NRTIs). As a result, the standard of care became two nucleosides, typically AZT+ddI (didanosine), or AZT+ddC (ditiocarb sodium). Dual combination therapy provided increased control of viral replication, made it more difficult for the virus to develop resistant strains or mutations and, as a result, provided extended clinical benefit to patients.
In 1995, another milestone was reached with the approval of the first of the protease inhibitors (PIs). These inhibitors showed greater potency than the nucleosides, but again were prone to resistance when used alone. Their combination with two nucleoside analogues, however, seemed to provide the control over the virus that everyone had been looking for. Triple combination therapy using two nucleosides (most commonly AZT+3TC) plus a protease inhibitor (typically indinavir) still remains the most common standard of care in developed countries.
These highly active combinations have had an enormous effect on the quality of life and on the survival of patients. This has resulted in fewer hospitalizations and reintegration of the patients in society. In a considerable number of patients, the viral load has been reduced to below the detection limit for prolonged periods.
In recent years, however, it has become clear that even patients being treated with triple therapy including a protease inhibitor often eventually experience treatment failure. Data suggests that up to one half of patients on combination therapy do not achieve or do not maintain suppression of virus replication. In some cases, it may be that even state-of-the-art triple therapy is insufficient to halt viral replication. As a result, drug resistant strains of the virus develop.
Another factor contributing to the difficulty to maintain suppression of virus replication has been the sheer burden of taking up to 20 pills each day, at set times, with or without food, day after day. It is simply unrealistic to expect people to adhere to such stringent and demanding regimens indefinitely. But if patients do not adhere, the price can be high. A dip in the blood levels of any of the medications gives the virus an opportunity to replicate and develop drug resistant strains. As such, during the course of infection, drug resistant viral strains can emerge very rapidly particularly for retroviral infections such as HIV-1. In addition, not all HIV-1 infections originate with a wild type, drug sensitive strain from which drug resistance will emerge. With the increase in prevalence of drug resistant strains comes the increase in infections that actually begin with drug resistant strains. Infections with pre-existing drug resistance immediately reduce the drug options for drug treatment and emphasize the importance of drug resistance information to optimize initial therapy for these patients.
Moreover, as the number of available antiretroviral agents has increased, so has the number of possible drug combinations and combination therapies. However, it is not easy for the health care provider to establish the optimal combination for an individual. Previously, the only treatment guidelines that have been in widespread use have been based on viral load and, where available, the patient's treatment history. The health care provider's objective is to keep the viral load as low as possible. An increase in viral load is a warning that control of viral replication is being lost and that a change in therapy is required. Viral load, however, provides no information or guidance regarding which drugs should be used.
Knowledge of the resistance patterns of different inhibitors and the patient's treatment history can help. Resistance emergence is highly predictive of treatment failure. In fact, while there are a variety of factors that can contribute to the failure of drug therapy, HIV-1 drug resistance is almost always involved. However, the interactions between different viral mutations related to different inhibitors is so complex that selecting the optimal treatment combination with only a treatment history to go on is far from ideal. Drugs can be ruled out unnecessarily and ineffective drugs can be introduced. Even if the virus is resistant to just one of three drugs in a treatment regimen, this can allow low-level viral replication to take place and viral strains resistant to the other two drugs to develop.
It is clear that although there are many drugs available for use in combination therapy, the choices can quickly be exhausted and the patient can rapidly experience clinical deterioration if the wrong treatment decisions are made. The key to tailored, individualized therapy lies in the effective profiling of the individual patient's virus population in terms of sensitivity or resistance to the available drugs. This will mean the advent of truly individualized therapy.
The aim of resistance monitoring is to provide the necessary information to enable the health care provider to prescribe the most optimal drug combination for the individual patient. At present, there are two distinct approaches to measuring resistance:
The first approach involves phenotyping, which directly measures the actual sensitivity of a patient's pathogen or malignant cell to particular therapies. For example, HIV-1 phenotype testing directly measures HIV-1 drug resistance, detected as the ability of HIV-1, taken from a patient, to grow in the presence of a drug, in the laboratory. The phenotype is measured or expressed in, for example, IC50 for a particular drug, which is defined as the concentration of drug required to kill half of the virions in a sample. This is compared to the IC50 for the drug using wild type virus. The phenotype may be described, but is not limited to, fold increase in IC50 for each of the drugs.
There are three main types of methodology for phenotyping. One such type is the plaque reduction assay. A drawback of this method is that it does not detect NSI strains. Another method of phenotyping includes PBMC p24 growth inhibition assays (Japour, A. J., Mayers, T. L., Johnson, V. A., Kuritzkes, D. R., Beckett, L. A., Arduino, J.-M., Lane, J., Black, R. J., Reichelderfer, P. S., D'Aquila, R. T., Crumpacker, C. S., The RV-43 Study Group & The ACTG Virology Committee Resistance Working Group. Antimicrob. Agents Chemother. 37, 1095–1101 (1993), incorporated by reference herein). A problem with this technique is that virus culture from PBMCs is very slow and labor-intensive. In addition, it lacks the precision of other techniques and because it relies on primary human cells for virus growth, assay automation and high throughput is virtually impossible. Yet another method is the recombinant virus assay (Kellam, P. & Larder, B. A. Antimicrob. Agents Chemother. 38, 23–30 (1994), incorporated by reference herein.). The recombinant method has advantages over the previously mentioned assays in that it reduces the amount of selection that takes place during growth of the virus in the laboratory, it is faster, more reproducible, amendable to automation and high throughput, and all available drugs can be tested in one assay.
The second approach to measuring resistance involves genotyping tests that detect specific genetic changes (e.g. but not limited to, mutations) in the viral genome, which lead to amino acid changes in at least one of the target proteins, known or suspected to be associated with resistance.
There are a number of techniques for conducting genotyping, such as hybridization-based point mutation assays and DNA sequencing. Common point mutation assays include Primer-specific PCR (Larder B A, Kellam P & Kemp, S D 1991. AIDS 5: 137–144, incorporated by reference herein.), differential hybridization (Eastman, P. S., Urdea, M., Besemer, D., Stempien, M. & Kolberg, J. 1995. J. Acquir. Immune Defic. Syndr. Human Retrovirol. 9, 264–273, incorporated by reference herein.), Line Probe Assay (LiPA®, Innogenetics) (Stuyver, L., Wyseur, A., Rombout, A., Louwagie, J., Scarcez, T., Verhofstede, C., Rimiand, D., Schinazi, R. F. & Rossau, R. 1997. Antimicrob. Agents Chemotherap. 41, 284–291, incorporated by reference herein.), and gene chip sequencing (Affymetrix) (D'Aquila, R. T. 1995. Clin. Diagnost. Virol. 3, 299–316, incorporated by reference herein.). Point mutation assays can only provide a small select part of the resistance picture. DNA sequencing, however, provides information on all the nucleotides in the region of the genome sequenced. This means that changes in the genome can be detected. It also means that, in contrast to point mutation assays, as new resistance mutations are found to be involved in the development of HIV-1 drug resistance, these can still be detected without adaptation of the technology (unlike point mutation assays).
However, at present, it remains difficult to interpret the results of a genotypic test to provide meaningful conclusions about therapy resistance. The advantage of phenotyping over genotyping is that phenotyping is a direct measure of any change in sensitivity resulting from all the mutations that have occurred, and any interactions between them. As such, it is the gold standard of resistance testing. Disadvantages of phenotyping are that it is complex, lengthy to perform, (usually 4 weeks) and, therefore, more expensive than genotyping. Thus, phenotyping is not a practical way of designing patient therapy.
The importance of the speed by which a health care provider can be informed of the patient's resistance profile can be demonstrated by the following hypothetical but realistic example, which highlights the need to reduce complexity and improve performance time of assessing resistance. Suppose first-line triple combination therapy reduces the viral load to undetectable limits for a period of time. The viral load then begins to increase as a result of the development of resistance. Without resistance information, the health care provider can make a judgement based on the patient's treatment history, and change one or more of the drugs. As a result viral load is, again, reduced but the new treatment regimen is sub-optimal so viral replication continues under selection pressure from the drugs and resistance rapidly develops once more. Consequently, control of viral replication is lost and several of the 15 drugs available have been ‘used up’.
Although genotyping tests can be performed more rapidly, a problem with genotyping is that there are now over 100 individual mutations with evidence of an effect on susceptibility to HIV-1 drugs and new ones are constantly being discovered, in parallel with the development of new drugs and treatment strategies. The relationship between these point mutations, deletions and insertions and the actual susceptibility of the virus to drug therapy is extremely complex and interactive. An example of this complexity is the M184V mutation that confers resistance to 3TC but reverses AZT resistance. The 333D/E mutation, however, reverses this effect and can lead to dual AZT/3TC resistance.
Consequently, the interpretation of genotypic data is both highly complex and critically important. There have been a number of different approaches to this challenge of interpretation. For example, armed with the knowledge of the main resistance mutations associated with each drug and the patient's recent treatment history, a health care provider makes a decision as to the optimum treatment. To assist health care providers to make these judgments, various expert opinion panels have been convened and have published guidelines, e.g. the Resistance Collaborative Group. In addition, rules-based algorithms constitute another approach. This is essentially a formalized version of the above with tables giving the mutations which are associated with resistance to each of the drugs. These can be simple printed tables or the information can be used to develop a rules-based computer algorithm. However, given the large number of mutations that are involved in resistance to antiretroviral drugs and given the complex interactions between the mutations, the shortcoming of genotyping is the reliable interpretation and clinical application of the results. As more drugs become available and as more mutations are involved in the development of resistance, the ‘manual’ or rules-based interpretation of raw genotype data is rapidly becoming impossible due to an increase in complexity.
Therefore, the main challenge involved with genotyping is improving the interpretation of the results. The technology will identify some (i.e., point mutation assays) or all of the mutations (i.e., DNA sequencing) that have occurred but it then requires sophisticated interpretation to predict what the net effect of these mutations might be on the susceptibility of the virus population to the various therapies. A health care provider might then have to combine this information with all the other information relating to the patient and decide what all this means in terms of selecting drugs for the treatment of their individual patient.